15ea23d5d6
* feat: introduce hindsight-api-slim and hindsight-all-slim packages Closes #552 - Move all source code from hindsight-api/ to new hindsight-api-slim/ - hindsight-api-slim has heavy ML deps (torch, sentence-transformers, transformers, einops, flashrank, mlx, mlx-lm, safetensors) and pg0-embedded as optional extras: [local-ml], [embedded-db], [all] - hindsight-api becomes a zero-code meta-package depending on hindsight-api-slim[all] for full backward compatibility - Add hindsight-all-slim meta-package: hindsight-api-slim + client + embed - hindsight-all updated to depend on hindsight-api-slim[all] - pg0.py: lazy-import pg0 with clear ImportError pointing to [embedded-db] - Dockerfile: replace sed hack with proper uv sync --extra flags - Update release.yml, test.yml, lint.sh, release.sh, CLAUDE.md and all path references throughout the repo * refactor: rename hindsight/ directory to hindsight-all/ * docs: document hindsight-api-slim and hindsight-all-slim package variants Add package variants table and extras explanation to installation.md * docs: remove emojis from installation.md, use professional tone * docs: link Docker slim variant to pip package variants section * docs: consolidate Docker image variants into single table * ci: fix working-directory paths after package restructure - Replace all hindsight-api → hindsight-api-slim in test.yml - Replace hindsight → hindsight-all in test.yml - Add --extra embedded-db to test-embed API install step * ci: add local-ml and embedded-db extras to API sync steps These extras were previously implicit in the old hindsight-api package (which bundled everything). Now that hindsight-api-slim uses optional extras, we must explicitly request local-ml and embedded-db in CI. * ci: add API install step with embedded-db to test-embed smoke test The smoke test starts hindsight-api as a daemon, which requires pg0-embedded. Add a dedicated install step for hindsight-api-slim with embedded-db extra so the daemon can start successfully. * ci: remove --no-install-project when using optional extras When --no-install-project is combined with --extra, the optional deps are not installed because extras require the project to be active. Remove --no-install-project from steps that need local-ml or embedded-db. * ci: fix ordering of uv sync steps to preserve optional extras When uv sync runs for a different workspace member, it removes optional extras installed for other members. Fix by always running extra-requiring API sync last, after other workspace member syncs. Also remove --no-install-project from embedded-db sync in test-embed, as --no-install-project prevents optional extras from being active. * ci: add local-ml extra to test-embed API install for smoke test The smoke test starts the full API server which needs sentence-transformers for local embeddings (default provider). Add local-ml extra to the install. * ci: simplify extras with --all-extras and add slim pip smoke test - Replace explicit --extra local-ml --extra embedded-db with --all-extras for cleaner, more maintainable sync steps - Add test-pip-slim job: tests hindsight-api-slim[embedded-db] without local ML models, using Cohere for embeddings/reranking (mirrors Docker slim smoke test approach) * ci: simplify slim smoke test to health check only (mirrors Docker test)
73 lines
2.1 KiB
Python
73 lines
2.1 KiB
Python
"""
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Utility functions for memory system.
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"""
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import logging
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from datetime import datetime
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from typing import TYPE_CHECKING
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if TYPE_CHECKING:
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from .llm_wrapper import LLMConfig
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from .retain.fact_extraction import Fact
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from .retain.fact_extraction import extract_facts_from_text
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async def extract_facts(
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text: str,
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event_date: datetime,
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context: str = "",
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llm_config: "LLMConfig" = None,
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agent_name: str = None,
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config=None,
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) -> tuple[list["Fact"], list[tuple[str, int]]]:
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"""
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Extract semantic facts from text using LLM.
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Uses LLM for intelligent fact extraction that:
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- Filters out social pleasantries and filler words
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- Creates self-contained statements with absolute dates
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- Handles conversational text well
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- Resolves relative time expressions to absolute dates
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Args:
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text: Input text (conversation, article, etc.)
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event_date: Reference date for resolving relative times
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context: Context about the conversation/document
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llm_config: LLM configuration to use
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agent_name: Optional agent name to help identify agent-related facts
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config: HindsightConfig to use (defaults to global config if not provided)
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Returns:
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Tuple of (facts, chunks) where:
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- facts: List of Fact model instances
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- chunks: List of tuples (chunk_text, fact_count) for each chunk
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Raises:
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Exception: If LLM fact extraction fails
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"""
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if not text or not text.strip():
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return [], []
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# Use provided config or fall back to global config
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if config is None:
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from ..config import _get_raw_config
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config = _get_raw_config()
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facts, chunks, _ = await extract_facts_from_text(
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text,
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event_date,
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llm_config=llm_config,
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agent_name=agent_name,
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config=config,
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context=context,
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)
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if not facts:
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logging.warning(
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f"LLM extracted 0 facts from text of length {len(text)}. This may indicate the text contains no meaningful information, or the LLM failed to extract facts. Full text: {text}"
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)
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return [], chunks
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return facts, chunks
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